Training Schedule Training Schedule

(4398) Implementing a SQL Data Warehouse
Code: 4398 v12/19/2018
Type: null
Tuition (USD): $2995
Length: 5 Days
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Location Date Guaranteed to Run
Live Virtual, US 04/08/2019
Live Virtual, US 05/06/2019
Live Virtual, US 05/20/2019
Live Virtual, US 06/10/2019

Not finding any suitable dates? Contact us for additional availability: training@dunnsolutions.com

Skills Gained:
1. Describe the key elements of a data warehousing solution 2. Describe the main hardware consideration for building a data warehouse 3. Implement a logical design for a data warehouse 4. Implement a physical design for a data warehouse 5. Create columnstore indexes 6. Implementing an Azure SQL Data Warehouse 7. Describe the key features of SSIS 8. Implement control flow by using tasks and precedence constraints 9. Create dynamic packages that include variables and parameters 10. Debug SSIS packages 11. Describe the considerations for implement an ETL solution 12. Implement Data Quality Services 13. Implement a Maser Data Services model 14. Describe how you can use custom components to extend SSIS 15. Deploy SSIS projects 16. Describe BI and common BI scenarios
Who Should Attend?
Database professionals who need to fulfill a BI developer role focused on hands-on work by creating BI solutions, including data warehouse implementation, ETL and data cleansing Database professionals responsible for implementing a data warehouse, developing SSIS packages for data ETL, enforcing data integrity using Microsoft Data Services and cleansing data using Data Quality Services
Prerequisites:
  • Basic knowledge of the Microsoft Windows operating system and its core functionality.
  • Working knowledge of relational databases.
  • Some experience with database design
  • Querying Data with Transact-SQL (20761)
What You Will Learn:
  • Classroom Live Outline
  • 1. Introduction to Data Warehousing
  • Overview of Data Warehousing
  • Considerations for a Data Warehouse Solution
  • 2. Planning Data Warehouse Infrastructure
  • Considerations for Building a Data Warehouse
  • Planning data warehouse hardware
  • 3. Designing and Implementing a Data Warehouse
  • Data warehouse design overview
  • Designing dimension tables
  • Designing fact tables
  • Physical Design for a Data Warehouse
  • 4. Columnstore Indexes
  • Introduction to Columnstore Indexes
  • Creating Columnstore Indexes
  • Working with Columnstore Indexes
  • 5. Implementing an Azure SQL Data Warehouse
  • Advantages of Azure SQL Data Warehouse
  • Implementing an Azure SQL Data Warehouse
  • Developing an Azure SQL Data Warehouse
  • Migrating to an Azure SQ Data Warehouse
  • Copying data with Azue data factory
  • 6. Creating an ETL Solution
  • Introduction ETL with SSIS
  • Exploring Source Data
  • Implementing Data Flow
  • 7. Implementing Control Flow in an SSIS Package
  • Introduction to Control Flow
  • Creating Dynamic Packages
  • Using Containers
  • Managing consistency
  • 8. Debugging and Troubleshooting SSIS Packages
  • Debugging an SSIS Package
  • Logging SSIS Package Events
  • Handling Errors in Data Flow
  • 9. Implementing a Data Extraction Solution
  • Introduction to Incremental ETL
  • Extracting Modified Data
  • Loading Modified Data
  • Temporal Tables
  • 10. Enforcing Data Quality
  • Introduction to Data Quality
  • Using Data Quality Services to Cleanse Data
  • Using Data Quality Services to Match Data
  • 11. Using Master Data Services
  • Introduction to Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Hierarchies and Collections
  • Creating a Master Data Hub
  • 12. Extending SQL Server Integration Services (SSIS)
  • Using Custom Components in SSIS
  • Using Scripting in SSIS
  • 13. Deploying and Configuring SSIS Packages
  • Overview of SSIS Deployment
  • Deploying SSIS Projects
  • Planning SSIS Package Execution
  • 14. Consuming Data in a Data Warehouse
  • Introduction to Business Intelligence
  • An Introduction to Data Analysis
  • Introduction to Reporting
  • Analyzing Data with Azure SQL Data Warehouse
  • Classroom Live Labs
  • Lab 1: Exploring a Data Warehouse Solution
  • Lab 2: Planning Data Warehouse Infrastructure
  • Lab 3: Implementing a Data Warehouse Schema
  • Lab 4: Using Columnstore Indexes
  • Lab 5: Implementing an Azure SQL Data Warehouse
  • Lab 6: Implementing Data Flow in an SSIS Package
  • Lab 7: Implementing Control Flow in an SSIS Package
  • Lab 8: Using Transactions and Checkpoints
  • Lab 9: Debugging and Troubleshooting an SSIS Package
  • Lab 10: Extracting Modified Data
  • Lab 11: Loading a data warehouse
  • Lab 12: Cleansing Data
  • Lab 13: De-duplicating Data
  • Lab 14: Implementing Master Data Services
  • Lab 15: Using Scripts
  • Lab 16: Deploying and Configuring SSIS Packages
  • Lab 17: Using a Data Warehouse
Course Info:
In this course, provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure, and covers installing from new and migrating from an existing install. This course incorporates material from the Official Microsoft Learning Product 20767: and it can assist you in your preparation for Exam 70-767: Implementing a SQL Data Warehouse.

This course is also available in the On-Demand delivery format with digital Microsoft Official Courseware (dMOC).

RELATED CERTIFICATIONS
Earning an MCSA: SQL 2016 Business Intelligence Development certification validates your extract, transform, and load (ETL) and data warehouse skills, along with those for implementing BI solutions using multidimensional and tabular data models and online analytical processing (OLAP) cubes. This certification will qualify you for a position as a BI developer.

MCSA: SQL 2016 Business Intelligence Development

Follow-On Courses:
None

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Practice Labs
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Indexed Class Recordings
Unlimited Course Retakes
Digital courseware

* Terms and conditions may apply. Learn more about our Microsoft training exclusives by contacting training@dunnsolutions.com

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Learn to implement a data warehouse platform with Microsoft SQL Server 2016.